To mitigate and alleviate low wheel-rail adhesion,a train-borne system is utilised to deposit sand particles into the wheel-rail interface via a jet of compressed air in a process called rail-sanding.Britain Rail Safe...To mitigate and alleviate low wheel-rail adhesion,a train-borne system is utilised to deposit sand particles into the wheel-rail interface via a jet of compressed air in a process called rail-sanding.Britain Rail Safety and Standards Board introduced guidelines on the sand particles’shape,size,and uniformity which needs to be adhered to for rail-sanding.To further inves-tigate these guidelines and help improve them,this research presents a parametric study on the particle characteristics that affect the rail-sanding process including density,size and size distribution,coefficient of uniformity,and shape,utilising a coupled computational fluid dynamics-discrete element method(CFD-DEM)model.The efficiency of rail-sanding is esti-mated for each case study and compared to the benchmark to optimise the sand characteristics for rail-sanding.It is concluded that particle size distribution(within the accepted range)has an insignificant effect on the efficiency while increasing particle size or the coefficient of uniformity decreases the efficiency.Particle shape is shown to highly affect the efficiency for flat,compact and elongated particles compared to the spherical shape.The current numerical model is capable of accurately predicting the trends in the efficiency compared to the actual values obtained from full-scale experiments.展开更多
Suffusion in broadly graded granular soils is caused by fluid flow and is a typical cause of geo-hazards.Previous studies of it have mainly focused on suffusion in homogeneous soil specimens.In this study,the coupled ...Suffusion in broadly graded granular soils is caused by fluid flow and is a typical cause of geo-hazards.Previous studies of it have mainly focused on suffusion in homogeneous soil specimens.In this study,the coupled discrete element method(DEM)and computational fluid dynamics(CFD)approach is adopted to model suffusion in multi-layered soils with different fines contents,and soils with one or more impermeable zones.The parameters of the CFD-DEM model are first calibrated with the classic Ergun test and a good match with experiment is obtained.Then suffusion in multi-layered soils with different fines contents and impermeable zones is simulated and discussed.The simulation results show that,for soils with multiple layers,the cumulative eroded mass is mainly determined by the fines content of the bottom layer.In general,the higher the fines content of the bottom soil layer,the higher the cumulative eroded mass.In addition,suffusion is more severe if the fines content of the layer above is decreased.Impermeable zones inside soil specimens can increase the flow velocity around those zones,facilitating the migration of fine particles and intensifying suffusion.展开更多
This study numerically investigates fracture initiation and propagation during polymer-based solution injection under varying thermal conditions.A coupled computational fluid dynamics and discrete element method(CFD-D...This study numerically investigates fracture initiation and propagation during polymer-based solution injection under varying thermal conditions.A coupled computational fluid dynamics and discrete element method(CFD-DEM)framework is used to model non-Newtonian fluid flow through a granular medium.The rheology of shear-thinning fluids and fluid-particle heat transfer are modeled with temperature-dependent power-law parameters.The current model is validated by comparing fracture propagation behavior and peak pressures against the similar numerical study.The adequacy of the fluid-particle heat transfer model is confirmed by comparing the results with an analytical approach.The simulation results show that polymer concentration significantly influences fracturing behavior.Less concentrated,lower-viscosity fluids are more likely to create linear fracture paths with enhanced fluid infiltration.In contrast,fluids with higher polymer concentrations and viscosities tend to produce wider fractures characterized by greater particle displacement.An increase in the fluid temperature injected into the cooler medium leads to a reduction of fracture size for the 0.4%(w/w)XG solution,while the 0.6%(w/w)XG solution tends to form more linear fracture tips.At sufficiently elevated medium temperatures,the injection of cooler fluids prevents fracture initiation for both concentrations.Lower-viscosity cases,dominated by infiltration,reflect broader thermal transitions in particle temperature distribution,whereas higher-viscosity cases,characterized by particle displacement,exhibit narrower transition regions along fracture boundaries.A fracture initiation criterion for shear-thinning fluids is proposed based on the dimensionless parametersΠ_(1)andτ_(2).Fracture occurs whenΠ_(1)>73 andτ_(2)>3.58×10^(−9).The 0.4%solution exhibits lower thermal sensitivity with relatively minimal variations in the dimensionless parameters,while the 0.6%solution shows a greater response to temperature changes,reflected in broader variations of these parameters.展开更多
We numerically study the mechanisms and conditions for fracture initiation in weakly cohesive granular media induced by non-Newtonian polymer solutions.A coupled computational fluid dynamics–discrete element method(C...We numerically study the mechanisms and conditions for fracture initiation in weakly cohesive granular media induced by non-Newtonian polymer solutions.A coupled computational fluid dynamics–discrete element method(CFD-DEM)approach is utilized to model fluid flow in a porous medium.The flow behavior of polymer solutions and the drag force acting on particles are calculated using a power-law model.The adequacy of the numerical model is confirmed by comparing the results with a laboratory experiment.The numerical results are consistent with the experimental data presenting similar trends in dimensionless parameters that incorporate fluid flow rate,rheology,peak pressure,and confining stress.The results show that fluid flow rate,rheology,and solid material characteristics strongly influence fracture initiation behavior.Injection of a more viscous guar-based solution results in wider fractures induced by grain displacement,whereas a less viscous XG-based solution creates more linear fractures dominated by infiltration.The ratio of peak pressures between two fluids is higher in the rigid material than in the softer material.Finally,the dimensionless parameters 1/Π_(1) and τ_(2),which account for fluid and solid material properties accordingly,are effective indicators in determining fracture initiation induced by shear-thinning fluids.Our numerical results show that fracture initiation occurs above 1/Π_(1)=0.06 and τ_(2)=2⋅10^(−7).展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
Solid-liquid fluidized beds of binary mixtures are widely used in many industries.Particle segregation may occur as particles can differ in size,density,or shape.Extensive studies have been conducted in the past to un...Solid-liquid fluidized beds of binary mixtures are widely used in many industries.Particle segregation may occur as particles can differ in size,density,or shape.Extensive studies have been conducted in the past to understand the effects of particlesize and density on the mixing and segregation,but the effect of particle shape has not been well addressed.Therefore,in the present work,CFD-DEM approach is employed to perform a numerical analysis of the effect of particle shape on the particle mixing and segregation phenomenon in liquid fluidization system.Different particle shapes from oblate to prolate are produced by varying aspect ratio of ellipsoids from 0.25 to 3,and eight binary mixtures of spheres and ellipsoids are examined.The results show that when oblate or prolate particles are added to spheres,the segregation takes place.The segregation degree increases with particle aspect ratio diverging from 1.0 and also liquid superficial velocity.The relationship of mixing index with aspect ratio under different liquid velocities is established,and a detailed explanation is given.It is revealed that increasing the projected area and hence the drag force results in the separation of ellipsoidal particles from spheres.展开更多
基金funded by the UK Engineering and Physical Sciences Research Council (EPSRC) grant No. EP/V053655/1 RAILSANDING-Modelling Particle Behaviour in the Wheel–Rail Interface the support received from AltairEngineering and Physical Sciences Research CouncilEP/V053655/1, Sadegh Nadimi
文摘To mitigate and alleviate low wheel-rail adhesion,a train-borne system is utilised to deposit sand particles into the wheel-rail interface via a jet of compressed air in a process called rail-sanding.Britain Rail Safety and Standards Board introduced guidelines on the sand particles’shape,size,and uniformity which needs to be adhered to for rail-sanding.To further inves-tigate these guidelines and help improve them,this research presents a parametric study on the particle characteristics that affect the rail-sanding process including density,size and size distribution,coefficient of uniformity,and shape,utilising a coupled computational fluid dynamics-discrete element method(CFD-DEM)model.The efficiency of rail-sanding is esti-mated for each case study and compared to the benchmark to optimise the sand characteristics for rail-sanding.It is concluded that particle size distribution(within the accepted range)has an insignificant effect on the efficiency while increasing particle size or the coefficient of uniformity decreases the efficiency.Particle shape is shown to highly affect the efficiency for flat,compact and elongated particles compared to the spherical shape.The current numerical model is capable of accurately predicting the trends in the efficiency compared to the actual values obtained from full-scale experiments.
基金This work is supported by the Research Grants Council(RGC)of Hong Kong(No.15226322)the National Natu‐ral Science Foundation of China(No.42207210).
文摘Suffusion in broadly graded granular soils is caused by fluid flow and is a typical cause of geo-hazards.Previous studies of it have mainly focused on suffusion in homogeneous soil specimens.In this study,the coupled discrete element method(DEM)and computational fluid dynamics(CFD)approach is adopted to model suffusion in multi-layered soils with different fines contents,and soils with one or more impermeable zones.The parameters of the CFD-DEM model are first calibrated with the classic Ergun test and a good match with experiment is obtained.Then suffusion in multi-layered soils with different fines contents and impermeable zones is simulated and discussed.The simulation results show that,for soils with multiple layers,the cumulative eroded mass is mainly determined by the fines content of the bottom layer.In general,the higher the fines content of the bottom soil layer,the higher the cumulative eroded mass.In addition,suffusion is more severe if the fines content of the layer above is decreased.Impermeable zones inside soil specimens can increase the flow velocity around those zones,facilitating the migration of fine particles and intensifying suffusion.
基金the support of the research grant no.AP19575428,from the Ministry of Science and Higher Education of the Republic of Kazakhstanthe support of the Nazarbayev University Faculty Development Competitive Research Grant(NUFDCRG),Grant No.20122022FD4141。
文摘This study numerically investigates fracture initiation and propagation during polymer-based solution injection under varying thermal conditions.A coupled computational fluid dynamics and discrete element method(CFD-DEM)framework is used to model non-Newtonian fluid flow through a granular medium.The rheology of shear-thinning fluids and fluid-particle heat transfer are modeled with temperature-dependent power-law parameters.The current model is validated by comparing fracture propagation behavior and peak pressures against the similar numerical study.The adequacy of the fluid-particle heat transfer model is confirmed by comparing the results with an analytical approach.The simulation results show that polymer concentration significantly influences fracturing behavior.Less concentrated,lower-viscosity fluids are more likely to create linear fracture paths with enhanced fluid infiltration.In contrast,fluids with higher polymer concentrations and viscosities tend to produce wider fractures characterized by greater particle displacement.An increase in the fluid temperature injected into the cooler medium leads to a reduction of fracture size for the 0.4%(w/w)XG solution,while the 0.6%(w/w)XG solution tends to form more linear fracture tips.At sufficiently elevated medium temperatures,the injection of cooler fluids prevents fracture initiation for both concentrations.Lower-viscosity cases,dominated by infiltration,reflect broader thermal transitions in particle temperature distribution,whereas higher-viscosity cases,characterized by particle displacement,exhibit narrower transition regions along fracture boundaries.A fracture initiation criterion for shear-thinning fluids is proposed based on the dimensionless parametersΠ_(1)andτ_(2).Fracture occurs whenΠ_(1)>73 andτ_(2)>3.58×10^(−9).The 0.4%solution exhibits lower thermal sensitivity with relatively minimal variations in the dimensionless parameters,while the 0.6%solution shows a greater response to temperature changes,reflected in broader variations of these parameters.
文摘We numerically study the mechanisms and conditions for fracture initiation in weakly cohesive granular media induced by non-Newtonian polymer solutions.A coupled computational fluid dynamics–discrete element method(CFD-DEM)approach is utilized to model fluid flow in a porous medium.The flow behavior of polymer solutions and the drag force acting on particles are calculated using a power-law model.The adequacy of the numerical model is confirmed by comparing the results with a laboratory experiment.The numerical results are consistent with the experimental data presenting similar trends in dimensionless parameters that incorporate fluid flow rate,rheology,peak pressure,and confining stress.The results show that fluid flow rate,rheology,and solid material characteristics strongly influence fracture initiation behavior.Injection of a more viscous guar-based solution results in wider fractures induced by grain displacement,whereas a less viscous XG-based solution creates more linear fractures dominated by infiltration.The ratio of peak pressures between two fluids is higher in the rigid material than in the softer material.Finally,the dimensionless parameters 1/Π_(1) and τ_(2),which account for fluid and solid material properties accordingly,are effective indicators in determining fracture initiation induced by shear-thinning fluids.Our numerical results show that fracture initiation occurs above 1/Π_(1)=0.06 and τ_(2)=2⋅10^(−7).
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
文摘Solid-liquid fluidized beds of binary mixtures are widely used in many industries.Particle segregation may occur as particles can differ in size,density,or shape.Extensive studies have been conducted in the past to understand the effects of particlesize and density on the mixing and segregation,but the effect of particle shape has not been well addressed.Therefore,in the present work,CFD-DEM approach is employed to perform a numerical analysis of the effect of particle shape on the particle mixing and segregation phenomenon in liquid fluidization system.Different particle shapes from oblate to prolate are produced by varying aspect ratio of ellipsoids from 0.25 to 3,and eight binary mixtures of spheres and ellipsoids are examined.The results show that when oblate or prolate particles are added to spheres,the segregation takes place.The segregation degree increases with particle aspect ratio diverging from 1.0 and also liquid superficial velocity.The relationship of mixing index with aspect ratio under different liquid velocities is established,and a detailed explanation is given.It is revealed that increasing the projected area and hence the drag force results in the separation of ellipsoidal particles from spheres.
